Technical Report 42: Including Medicare Cost Data in VA Research

Suggested Citation

Illarmo S, Gujral K. Including Medicare Cost Data in VA Research. Technical Report 42. Health Economics Resource Center, U.S. Department of Veterans Affairs. September 2023. https://www.herc.research.va.gov/include/page.asp?id=technical-report-42. 

 

For a list of VA acronyms, please visit the VA acronym checker on the VA intranet at http://vaww.va.gov/Acronyms/fulllist.cfm.

Highlights

• VA patients often obtain additional care outside the VA system; dual use is particularly common among Veterans eligible for Medicare.
• Many VA studies would be enhanced by including Medicare data.
• This technical report includes common reasons for including Medicare data in VA studies with examples from the literature.

Background

VA patients often obtain additional care outside the VA system through Medicare, Medicaid, or other forms of insurance. This “dual use” is particularly common among Veterans eligible for Medicare. In FY2018, 50% of VA enrollees were dually enrolled in Medicare, with another 4% enrolled in VA, Medicare, and Medicaid.¹ Therefore, researchers hoping to gain a more complete picture of their cohort’s health care use and costs may want to include Medicare data in their analyses.

There are many research questions that would benefit from the inclusion of Medicare data. Below, we provide examples of reasons VA researchers may want to include Medicare data in their analyses, with examples from recent literature.[1]

1. A more comprehensive understanding of Veteran health care utilization and costs

A common reason for including CMS data is to gain a more comprehensive understanding of Veteran health care utilization and costs.

Studies that analyze costs: The studies included here combine costs in VA and Medicare data to calculate total costs. Studies (with the exception of Wong et al. 2021) also include VA Community Care data in their cost calculations. While the majority of studies used HERC average cost data to determine VA costs, other studies used MCA cost data or the GECDAC core files.

• Yoon, Pal Chee et al. (2018) used Medicare and VA data to evaluate factors associated with persistence in high costs over time for high-cost VA patients. They used HERC average cost data to determine the cost of VA care, Community Care data for VA purchased care, and BASF and MBSF data for Medicare costs. They found that most high-cost patients do not remain high cost for over a year, largely due to high mortality rates among high-cost patients.
• Gidwani-Marszowski et al. (2019) used VA and Medicare data to quantify the costs of intensive end-of-life medical services from both a health system and beneficiary perspective. The used MCA data for VA health system costs and VA beneficiary costs were calculated using VA copayment rates. Medicare health system and beneficiary costs were pulled from the Medicare MedPAR, Part D, and claims-level datasets. They included VA Fee Basis data for community care costs. They found the additional cost of medically intensive services to the health system was $15,952, with the biggest contributor being ICU stays, and the additional mean cost to the beneficiary in the last month of life was $1,124.
• Lei et al (2021) used the GECDAC core files, which combine VA data (for VA provided and VA purchased care) with Medicare data for a cohort of aging and frail Veterans, to evaluate continuity of care and its impact on costs. They found that increased continuity of care resulted in lower total costs.
• Mor et al. (2019) examined whether VA’s Comprehensive End-of-Life Care Initiative, which allows Veterans to access cancer treatment at VA concurrently with hospice, was associated with reduced aggressive end-of-life care and costs. They used HERC average cost data to estimate VA costs. To estimate non-VA costs, they used VA Fee Basis and Medicare reimbursements. They found that concurrent cancer and hospice care was associated with less aggressive care and significantly lower costs, with no difference in the odds of survival.
• Wong et al. (2021) compared the downstream medical costs of patients treated with direct oral anticoagulants vs warfarin. They use HERC average cost data and MCA pharmacy data to determine VA costs, and they used Medicare claims and Part D data to determine Medicare costs. The found that patients treated with warfarin had higher downstream costs than those treated with direct oral anticoagulants.
• Wang (2020) looked at the impact of VA payment reform policies for VA-paid community care dialysis on cost and outcomes. They used VA, Medicare, and US Renal Data System data to identify community care dialysis before and after payment reform. They found that after payment reform, there was a reduction in price per session, an increase in the number of facilities contracting with VA, and no changes in patient outcomes.

Studies that include utilization data only: Dennis (2023), Edwards (2022), Greene (2022) and Baldomero (2022) are examples of studies that combine utilization across VA-provided care, VA community care, and Medicare. Richardson (2023) combines utilization data across VA, Medicare, and Medicaid.

• Dennis et al. (2023) used VA and Medicare utilization data to evaluate how home time impacted patient-reported quality of life to improve measures of home time They found that setting of care affected quality of life, with post-acute care facility utilization within the last 6 months having the greatest association with decreased quality of life.
• Edwards et al. (2022) combined VA and Medicare data to examine care fragmentation and the impact of fragmentation on patient outcomes, focusing on outcomes of ED visits and hospitalizations for ambulatory care sensitive conditions. They found patients with more fragmented care had higher rates of ED visits and hospitalizations.
• Greene et al. (2022) evaluated the association between patient-reported social risk, behavioral, and health factors with emergency department visits, using VA and Medicare data to measure emergency department utilization. They found patient-reported functional status, transportation problems, and self-rated health were significantly associated with ED visits.
• Baldomero et al. (2022) evaluated the impact of drive time on receipt of care, using VA and Medicare data to capture both VA and non-VA health care use. They found the odds of receiving recommended services decreased as drive time increased.
• Richardson et al. (2023) combined VA, Medicare and Medicaid data to understand the effectiveness of glucagon-like peptide-1 receptor agonists and sodium–glucose cotransporter-2 inhibitors in preventing major adverse cardiac events. They used a retrospective comparative effectiveness design to emulate a controlled trial.

2. Conducting economic evaluations and cost-effectiveness analyses

Health systems are often interested in understanding the value of the care they provide relative to the cost of providing that care. Such analyses are critical for informing policy decisions regarding which interventions a health system should implement, which treatment should be administered, and decisions related to whether a health system like VA should provide in-house care or reimburse Veterans for care outside VA. A comprehensive analysis that includes costs of VA care, VA-paid care in the community, and care received from other insurers such as Medicaid/Medicare for a Veteran cohort can significantly improve the relevance and scope of these analyses.

Cost Effectiveness Analysis (CEA) is one method for comparing the cost and effectiveness of two or more alternatives with the goal of determining whether the value of an intervention justifies the cost. CEAs include assigning a measure of value to the outcome, typically measured as a quality-adjusted life year. CEAs in VA may be conducted as a follow up to a randomized trial, with investigators combining effectiveness data from trial arms with administrative cost data.

• Stroupe et al (2012) evaluated mean health care costs per life year and per quality adjusted life year (QALY) for the two study arms from randomization to 2 years post-randomization. The used VA and Medicare claims data, along with patient self-report, to determine health care utilization and costs. For VA data, they used both MCA and HERC Average Cost data to determine costs. The estimated Medicare costs by multiplying the charges in the Medicare claims data by the hospital-specific cost-to charge ratio.
• Stroupe et al (2014) calculated the mean differences in costs and QALYs between the two study arms from randomization to 3 years post-randomization. They used VA, Medicare, and clinical trial data to calculate costs.
• Bansback et al (2017) evaluated the incremental cost-effectiveness ratios (ICERs) and QALYs of the two treatment arms, using clinical trial data to measure utilization and the Medicare fee schedule to determine unit costs.

Some economic evaluations may compare costs of care or focus on cost comparisons without juxtaposing them with the value of care, as value of care can be difficult to estimate. These studies are still informative with respect to health care expenditures and budget planning for a health system. Below, we include examples of cost comparisons using VA and Medicare data. 

• Van Aalst et al (2019) used IV methods to compare the economic impact of two different doses of the influenza vaccine on health care costs. They used data from the National Acquisition Center to determine the cost of the vaccines at VA and Medicare claims data for costs incurred outside VA.
• Wagner et al (2019) analyzed participants’ health care costs in a clinical trial follow up study for both study arms 5 years post-intervention. They used multivariate cost regressions (specifically, a general linear model with a log link and gamma distribution) to analyze costs. They used MCA data to identify VA costs and VA Community Care and Medicare Part A and B data for non-VA costs.
• Cai et al (2021) used a propensity score-matching approach to evaluate costs, both VA direct costs and VA and Medicare costs combined, as well as utilization, and mortality for VA’s Transfer Hospital in Home (T-HiH) program compared to a control group. They found that for the T-HiH group, VA costs were 20% lower and combined VA and Medicare costs were 22% lower than the control group.

Learn more about methods in our cost-effectiveness analysis seminar series. Causal inference methods can also be used to improve estimations in CEAs. Archived seminars on CEAs and causal inference/econometrics methods are available online.  

3. Understanding which system Veterans choose for different health care services

Understanding where Veterans choose to receive care by type of service and factors that may influence this decision (e.g., distance, wait time) can shape quality improvement efforts. It also has implications for allocating VA resources and VA’s decision about whether to continue providing a certain service at VA hospitals or purchase care from outside VA (i.e., make vs buy decisions). One measure VA researchers have employed to compare care between systems is patients’ reliance on VA, typically calculated as the proportion of care received at VA divided by the sum of all care included in the study (VA plus Medicare and/or Medicaid). In these studies, “VA-reliance” or “Medicare/Medicaid-reliance” typically refers to the system in which a patient receives the majority of their care for a given time period. Wong (2018), Hebert (2020), Vanneman (2022), and Yoon (2018 and 2019) are examples of studies that look at system use using a measure of reliance.

Researchers can measure the change in Veterans use of care between systems to understand the effect of a policy change such as Medicaid expansion (Yoon (2018 and 2019), O’Mahen (2020) or VA community care expansion (Vanneman 2022)).

• Wong et al. (2018) used VA, Medicare claims, and the Survey of Healthcare Experiences of Patients (SHEP) data to evaluate whether wait time effected patient reliance on VA primary care. They found that patients who reported longer wait times had lower reliance on VA.
• Hebert et al. (2020) used VA, Community Care, and Medicare data to examine Veterans’ reliance on VA or Medicare-paid care after specific events. They found that for Medicare-eligible VA-reliant Veterans, moving further from the VHA, having a Medicare-paid hospital stay, or receiving a life-threatening diagnosis for most conditions did not impact VA reliance.
• Vanneman et al. (2022) used VA, Community Care, and Medicare data to examine how drive distance and time impact reliance on VA for outpatient primary, specialty, and mental health care. They found that Veterans meeting MISSION Act drive time criteria had significantly lower odds of reliance on VA for specialty care. They found no association between drive distance or drive time on reliance for primary or mental health care.
• Yoon, Vanneman et al. (2018) used VA, Community Care, and Medicaid data to measure reliance on VA before and after enrollment in Medicaid. They found Medicaid enrollment did not have a significant impact on VA reliance. Yoon et al. (2019) also examined the proportion of VA-provided care by categories of outpatient and inpatient care; they include details of how they characterized care in the methods section of the manuscript. They found that patients relied on Medicaid for the majority of their inpatient care and VA for the majority of their outpatient care.
• O’Mahen et al. (2020) used VA and Medicaid data to evaluate how Medicaid expansion impacted VA and Medicaid dual enrollment and health care use. They found the expansion was associated with an increase in dual enrollment and reductions in the proportion of hospitalizations and emergency department occurring at VA for low-income Veterans.

4. Evaluating how system of care impacts outcomes

Comparing outcomes in VA to those in commercial hospitals (via Medicare claims) provides information about quality of care at VA. It can also offer evidence to guide policies around VA-purchased community care.

• Wachterman et al. (2022) used VA, Community Care, and Medicare data to evaluate whether payer of care (VA or Medicare) impacts the frequency of receipt of concurrent hospice and dialysis. They found that patients receiving VA-paid hospice (either at VA or through VA community care) were more likely to receive concurrent dialysis care.
• Chan et al. (2022) compared mortality for dually Medicare and VA-enrolled patients who experienced ambulance rides to VA hospitals vs non-VA hospitals. They found the mortality rate was lower for patients taken to VA hospitals.

5. Comparing care quality between VA and commercial hospitals

In addition to comparing outcomes, VA researchers can also use Medicare data to compare care quality between systems. Gidwani-Marszowski et al. (2018) used VA and Medicare data to compare the quality of care provided at the end-of-life for Veterans reliant on VA vs those reliant on Medicare. Using well-accepted metrics of overly intensive care, they found that Medicare-reliant Veterans were significantly more likely to receive overly intensive services for most metrics, with the exception of multiple emergency department visits.  

6. Comparing costs between VA and commercial hospitals

Cost data in VA and Medicare aren’t directly comparable due to differences in how costs are defined, and the elements included in the calculations of costs. Before undertaking a cost comparison, researchers should take steps to make the data more comparable and understand that even after doing so, there will be limitations with these comparisons. See the pages Comparing VA vs. Non-VA Costs and Medicare/Medicaid Cost Data for information about the differences between the two data sources.

• Gidwani et al. (2021) compared the cost trajectories (rather than dollar value) at the end-of-life for patients reliant on Medicare, VA, or both. They looked at trajectories for inpatient, outpatient, pharmacy, and total costs using MCA and Community Care data for VA costs. They made costs more comparable between systems by including only types of care provided by both systems (e.g., dropping domiciliary from VA costs). Although all three groups experienced different cost trajectories, all three groups experienced a dramatic rise in costs approaching death, largely due to increased inpatient costs.  
• Pickering et al. (2022) quantified the cost of Low-value PSA testing and associated downstream services and compared these costs for dually-enrolled Veterans who received their PSA test through VA vs through Medicare. They used HERC Average Cost data to estimate costs for care received through either system, rather than using the values from the health care claims, thereby ensuring costs are comparable between the two systems. They found that Veterans who received a PSA test through Medicare incurred an additional $36 per person while Veterans who received a PSA test through VA incurred an additional $25 per person.

7. Understanding prescription drug use across systems

Medicare-eligible Veterans often receive prescription drugs from both VA and Medicare. Therefore, combining these data can provide a more complete picture of prescription drug use, understand overlaps in prescribing, and highlight any prescription safety risks (Thorpe et al, 2019).

• Burke et al. (2023) categorized medications into drug class and looked at prescribing patterns overall and between VA and Medicare for dually enrolled Veterans. They found that gabapentinoid prescriptions increased across systems, while opioid and sedative-hypnotic prescription prevalence decreased. In the between system analysis, opioid and sedative-hypnotic prescription prevalence was higher in Medicare Part D, while percent of days covered for all drug classes was higher in VA.
• Radomski et al. (2019) used Medicare Part D and DME data, in combination with VA data, to modify a medication-based risk adjustment index for VA and Medicare. They primarily used National Drug Codes to match prescription drugs in VA and Medicare Part D; additional information in available in the appendix file of the manuscript.  
• VA researchers have evaluated the impact of Medicare and VA dual use on various prescriptions including high-risk opioid prescriptions (Chui et al. (2018)), overlapping opioid and benzodiazepine prescriptions (Carico et al. (2018)), and supply of antihypertensives (Thorpe et al. (2018)).
• Lei et al. (2021) looked at a cohort of VA and Medicare Part D dual users to evaluate Benzodiazepine (BZD) prescriptions in VA and Medicare and patient characteristics associated with benzodiazepine prescription outside VA. They found that, while overall BZD prescriptions declined, the majority of BDZ prescriptions were through Medicare, and older age was associated with higher odds of receiving a BZD prescription through Medicare.

Discussion

With more than half of VA enrollees dually enrolled in Medicare or Medicaid, researchers conducting analyses on health care use and related costs may be interested in including CMS data in their study. In this tech report, we have discussed 7 ways research inquiries may benefit from the inclusion of Medicare data. However, Medicare data is not the only source for non-VA health care. Many younger Veterans are enrolled in private insurance in addition to VA;² therefore, some researchers may want to consider including state all payer claims data in their analyses. Veterans may also be enrolled in Tricare,² so researchers may consider including DaVINCI data. While VA data offers a wealth of information to researchers, the inclusion of non-VA data, such as Medicare, can offer additional insights and enhance VA research.

Limitations: This report is not meant to be a comprehensive literature review on the use of VA and Medicare cost data. Papers were selected as illustrative examples of each research topic and were limited to those published 2018 or later (with the exception of CEAs with VA and Medicare data, which were limited to 2010 or later).

Example Studies

Studies of Health Care Costs

• Gidwani R, Asch SM, Needleman J, et al. End-of-Life Cost Trajectories in Cancer Patients Treated by Medicare versus the Veterans Health Administration. J Am Geriatr Soc. 2021 Apr;69(4):916-923.
• Gidwani-Marszowski R, Asch SM, Mor V, et al. Health System and Beneficiary Costs Associated With Intensive End-of-Life Medical Services. JAMA Netw Open. 2019 Sep 4;2(9):e1912161.
• Lei L, Intrator O, Conwell Y, et al. Continuity of care and health care cost among community-dwelling older adult veterans living with dementia. Health Serv Res. 2021 Jun;56(3):378-388.
• Mor V, Wagner TH, Levy C, et al. Association of Expanded VA Hospice Care With Aggressive Care and Cost for Veterans With Advanced Lung Cancer. JAMA Oncol. 2019 Jun 1;5(6):810-816.
• Pickering AN, Zhao X, Sileanu FE, et al. Assessment of Care Cascades Following Low-Value Prostate-Specific Antigen Testing Among Veterans Dually Enrolled in the US Veterans Health Administration and Medicare Systems. JAMA Netw Open. 2022 Dec 1;5(12):e2247180.
• Wang V, Swaminathan S, Corneau EA, et al. Association of VA Payment Reform for Dialysis with Spending, Access to Care, and Outcomes for Veterans with ESKD. Clin J Am Soc Nephrol. 2020 Nov 6;15(11):1631-1639.
• Wong ES, Done N, Zhao M, et al. Comparing total medical expenditure between patients receiving direct oral anticoagulants vs warfarin for the treatment of atrial fibrillation: evidence from VA-Medicare dual enrollees. J Manag Care Spec Pharm. 2021 Aug;27(8):1056-1066.
• Yoon J, Chee CP, Su P, et al. Persistence of High Health Care Costs among VA Patients. Health Serv Res. 2018 Oct;53(5):3898-3916.

Studies of Health Care Utilization

• Baldomero AK, Kunisaki KM, Wendt CH, et al. Drive Time and Receipt of Guideline-Recommended Screening, Diagnosis, and Treatment. JAMA Netw Open. 2022 Nov 1;5(11):e2240290.
• Chan DC, Danesh K, Costantini S, et al. Mortality among US veterans after emergency visits to Veterans Affairs and other hospitals: retrospective cohort study. BMJ. 2022 Feb 16;376:e068099.
• Dennis PA, Stechuchak KM, Van Houtven CH, et al. Informing a home time measure reflective of quality of life: A data driven investigation of time frames and settings of health care utilization. Health Serv Res. 2023 Jun 25.
• Edwards ST, Greene L, Chaudhary C, et al. Outpatient Care Fragmentation and Acute Care Utilization in Veterans Affairs Home-Based Primary Care. JAMA Netw Open. 2022 Sep 1;5(9):e2230036.
• Gidwani-Marszowski R, Needleman J, Mor V, et al. Quality Of End-Of-Life Care Is Higher In The VA Compared To Care Paid For By Traditional Medicare. Health Aff (Millwood). 2018 Jan;37(1):95-103.
• Greene L, Maciejewski ML, Grubber J, et al. Association between patient-reported social, behavioral, and health factors and emergency department visits in high-risk VA patients. Health Serv Res. 2023 Apr;58(2):383-391. doi: 10.1111/1475-6773.14094.
• Hebert PL, Wong ES, Reddy A, et al. Events Associated With Changes in Reliance on the Veterans Health Administration Among Medicare-eligible Veterans. Med Care. 2020 Aug;58(8):710-716.
• O’Mahen PN, Petersen LA. Effects of State-level Medicaid Expansion on Veterans Health Administration Dual Enrollment and Utilization: Potential Implications for Future Coverage Expansions. Med Care. 2020 Jun;58(6):526-533.
• Richardson TL Jr, Halvorson AE, Hackstadt AJ, et al. Primary Occurrence of Cardiovascular Events After Adding Sodium-Glucose Cotransporter-2 Inhibitors or Glucagon-like Peptide-1 Receptor Agonists Compared With Dipeptidyl Peptidase-4 Inhibitors: A Cohort Study in Veterans With Diabetes. Ann Intern Med. 2023 Jun;176(6):751-760.
• Vanneman ME, Yoon J, Singer SJ, et al. Anticipating VA/non-VA care coordination demand for Veterans at high risk for hospitalization. Medicine (Baltimore). 2022 Feb 18;101(7):e28864.
• Wachterman MW, Corneau EE, O'Hare AM, et al. Association of Hospice Payer With Concurrent Receipt of Hospice and Dialysis Among US Veterans With End-stage Kidney Disease: A Retrospective Analysis of a National Cohort. JAMA Health Forum. 2022 Oct 7;3(10):e223708.
• Wong ES, Liu CF, Hernandez SE, et al. Longer wait times affect future use of VHA primary care. Healthc (Amst). 2018 Sep;6(3):180-185.
• Yoon J, Vanneman ME, Dally SK, et al. Use of Veterans Affairs and Medicaid Services for Dually Enrolled Veterans. Health Serv Res. 2018 Jun;53(3):1539-1561.
• Yoon J, Vanneman ME, Dally SK, et al. Veterans' Reliance on VA Care by Type of Service and Distance to VA for Nonelderly VA-Medicaid Dual Enrollees. Med Care. 2019 Mar;57(3):225-229.

CEAs and other cost analyses

• Bansback N, Phibbs CS, Sun H, et al. Triple Therapy Versus Biologic Therapy for Active Rheumatoid Arthritis: A Cost-Effectiveness Analysis. Ann Intern Med. 2017 Jul 4;167(1):8-16.
• Cai S, Intrator O, Chan C, et al. Association of Costs and Days at Home With Transfer Hospital in Home. JAMA Netw Open. 2021 Jun 1;4(6):e2114920.
• Stroupe KT, Lederle FA, Matsumura JS, et al. Open Versus Endovascular Repair (OVER) Veterans Affairs Cooperative Study Group. Cost-effectiveness of open versus endovascular repair of abdominal aortic aneurysm in the OVER trial. J Vasc Surg. 2012 Oct;56(4):901-9.e2.
• Stroupe KT, Weaver FM, Cao L, et al. Cost of deep brain stimulation for the treatment of Parkinson's disease by surgical stimulation sites. Mov Disord. 2014 Nov;29(13):1666-74.
• van Aalst R, Russo EM, Neupane N, et al. Economic assessment of a high-dose versus a standard-dose influenza vaccine in the US Veteran population: Estimating the impact on hospitalization cost for cardio-respiratory disease. Vaccine. 2019 Jul 26;37(32):4499-4503.
• Wagner TH, Hattler B, Bakaeen FG, et al. Costs Five Years After Off-Pump or On-Pump Coronary Artery Bypass Surgery. Ann Thorac Surg. 2019 Jan;107(1):99-105.

Studies on Prescription Drugs

• Burke RE, Pelcher L, Tjader A, et al. Central Nervous System-Active Prescriptions in Older Veterans: Trends in Prevalence, Prescribers, and High-risk Populations. J Gen Intern Med. 2023 Jun 22.
• Carico R, Zhao X, Thorpe CT, et al. Receipt of Overlapping Opioid and Benzodiazepine Prescriptions Among Veterans Dually Enrolled in Medicare Part D and the Department of Veterans Affairs: A Cross-sectional Study. Ann Intern Med. 2018 Nov 6;169(9):593-601.
• Chui PW, Bastian LA, DeRycke E, et al. Dual Use of Department of Veterans Affairs and Medicare Benefits on High-Risk Opioid Prescriptions in Veterans Aged 65 Years and Older: Insights from the VA Musculoskeletal Disorders Cohort. Health Serv Res. 2018 Dec;53(Suppl 3):5402-5418.
• Lei L, Strominger J, Wiechers IR, et al. Benzodiazepine Prescribing from VA and Medicare to Dually Enrolled Older Veterans: A Retrospective Cohort Study. J Gen Intern Med. 2021 Dec;36(12):3689-3696.
• Radomski TR, Zhao X, Hanlon JT, et al. Use of a medication-based risk adjustment index to predict mortality among veterans dually-enrolled in VA and medicare. Healthc (Amst). 2019 Dec;7(4):S2213-0764(18)30230-6.
• Thorpe CT, Gellad WF, Mor MK, et al. Effect of Dual Use of Veterans Affairs and Medicare Part D Drug Benefits on Antihypertensive Medication Supply in a National Cohort of Veterans with Dementia. Health Serv Res. 2018 Dec;53 Suppl 3(Suppl Suppl 3):5375-5401.
• Thorpe JM, Thorpe CT, Schleiden L, et al. Association Between Dual Use of Department of Veterans Affairs and Medicare Part D Drug Benefits and Potentially Unsafe Prescribing. JAMA Intern Med. 2019 Nov 1;179(11):1584-1586.

References

1. de Groot, K., Kan, D., & Rowneki, M.  VA/CMS Data Snapshot: Veterans’ Enrollment in VHA, Medicare, and Medicaid during FY2018. Hines, IL: U.S. Dept. of Veterans Affairs, Health Systems Research Service, VA Information Resource Center. September 2021. Available at https://vaww.virec.research.va.gov/VACMS/Summary-Statistics/DataSnapshot-VHA-CMS-Enrollment-FY18.pdf (VA intranet only).
2. Cohen RA, Boersma P. Financial burden of medical care among veterans aged 25–64, by health insurance coverage: United States, 2019–2021. National Health Statistics Reports; no 182. Hyattsville, MD: National Center for Health Statistics. 2023. Available at https://www.cdc.gov/nchs/data/nhsr/nhsr182.pdf.